In recent years, magnetic resonance imaging has proven to be an important imaging
modality for diagnosing and locating pathology. Recent studies have shown that
multispectral tissue classification (MTC) may segment pathology from healthy tissues.
Several studies have been done to classify brain tissues such as white matter (WM), gray
matter (GM) and cerebrospinal fluid (CSF) using MTC with slice thicknesses ranging
from 5 to 10 mm (Kohn, 1991; Fletcher, 1993; Kao, 1994). In one of the previous studies
(Fletcher, 1993) MTC has been used to classify brain tissues such as WM, GM, CSF,
adipose tissue (AD), muscle (MS) and skin and meninges (S&M) with a slice thickness of
5 mm. The chosen slice thickness in the above mentioned studies is not quantified.
Therefore a question remains as to what is the optimum slice thickness for MTC of brain
tissues. The purpose of this research is to evaluate the ability of MTC to segment the
brain tissues as a function of slice thickness using spectral regions such as spin-lattice
relaxation time (TO, spin-spin relaxation time (T2), and spin density (p). The slice
thicknesses used in the study were 3 mm, 5 mm, and 10 mm.
Raw spin-echo images were acquired from a 39 year old volunteer at the level of
lateral ventricles through the brain on a General Electric (Milwaukee, WI) 1.5 Tesla
Signa imager with quadrature bird cage head coil. Ti, and p images were calculated from
a set of seven raw spin-echo images using non-linear least square procedure (Gong,
1992) with varying repetition time (TR) and a constant echo time (TE). Similarly T2
images were calculated from a set of eight raw images using linear least square fit
algorithm (Li, 1993) with varying TE and constant TR images. The Ti, T2, and p images
were calculated for 3, 5, and 10 mm slice thicknesses. The ability to segment tissues
WM, GM, CSF, AD, MS, and S&M as a function of slice thickness, was analyzed using
optimization parameters such as false positive ratio (FPR), false negative ratio (FNR),
true positive ratio (TPR), unclassified pixel ratio (UPR) and signal-to-noise ratio (S/N).
The effect of partial voluming and spatial resolution on tissue classification was also
evaluated. The optimum slice thickness for six brain tissue classification was
determined.